US20190294820A1 - Converting plaintext values to pseudonyms using a hash function - Google Patents
Converting plaintext values to pseudonyms using a hash function Download PDFInfo
- Publication number
- US20190294820A1 US20190294820A1 US15/926,392 US201815926392A US2019294820A1 US 20190294820 A1 US20190294820 A1 US 20190294820A1 US 201815926392 A US201815926392 A US 201815926392A US 2019294820 A1 US2019294820 A1 US 2019294820A1
- Authority
- US
- United States
- Prior art keywords
- hash
- value
- values
- plaintext
- pseudonym
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
- G06F21/6254—Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0618—Block ciphers, i.e. encrypting groups of characters of a plain text message using fixed encryption transformation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0894—Escrow, recovery or storing of secret information, e.g. secret key escrow or cryptographic key storage
Definitions
- the processing nodes 110 may be coupled to a storage 160 of the computer system 100 through network fabric (not depicted in FIG. 1 ).
- the network fabric may include components and use protocols that are associated with any type of communication network, such as (as examples) Fibre Channel networks, iSCSI networks, ATA over Ethernet (AoE) networks, HyperSCSI networks, local area networks (LANs), wide area networks (WANs), global networks (e.g., the Internet), or any combination thereof.
Abstract
Description
- A business organization (a retail business, a professional corporation, a financial institution, and so forth) may collect, process and/or store data that represents sensitive or confidential information about individuals or business organizations. For example, the data may be personal data that may represent names, residence addresses, medical information, salaries, banking information, and so forth. The data may be initially collected or acquired in “plaintext form,” and as such may be referred to as “plaintext data.” Plaintext data refers to ordinarily readable data. As examples, plaintext data may be a sequence of character codes, which represent the residence address of an individual in a particular language; or the plaintext data may be a number that that conveys, for example, a blood pressure reading.
-
FIG. 1 is a schematic diagram of a computer system according to an example implementation. -
FIG. 2 is a flow diagram depicting a technique to convert a plaintext value to a pseudonym value using hashes according to an example implementation. -
FIG. 3 is a statistical distribution of pseudonym values where each pseudonym value is generated using a single hash function iteration according to an example implementation. -
FIG. 4 is a statistical distribution of pseudonym values where each pseudonym value is generated using two hash function iterations according to an example implementation. -
FIG. 5 is a statistical distribution of pseudonym values where each pseudonym value is generated using three hash function iterations according to an example implementation. -
FIG. 6 is a flow diagram depicting a technique to use a hash function to provide a pseudonym value according to an example implementation. -
FIG. 7 is an illustration of a machine readable storage medium storing machine executable instructions to apply a one way conversion function to determine a pseudonym value according to an example implementation. -
FIG. 8 is a schematic diagram of an apparatus to convert a dataset representing plaintext data to a dataset representing pseudonyms based on hashes derived from the plaintext data according to an example implementation. - For purposes of controlling access to sensitive information (e.g., information relating to confidential or sensitive information about one or more business enterprises and/or individuals) plaintext data items, which represent the sensitive information, may be converted, through a process called “pseudonymization,” into corresponding pseudonymns, or pseudonym values. In this context, a “plaintext data item” (also referred to as “plaintext,” or a “plaintext value” herein) refers to a unit of data (a string, an integer, a real number, and so forth) that represents ordinarily readable content. As examples, a plaintext data item may be a string of character codes that corresponds to data that represents a number that conveys, in a particular number representation (an Arabic representation, for example), a blood pressure measurement, a salary, and so forth. The pseudonym value ideally conveys no information about the entity associated with the corresponding plaintext value. The pseudonymization process may or may not be reversible, in that reversible pseudonymization processes allow plaintext values to be recovered from pseudonym values, whereas irreversible pseudonymization processes do not.
- The pseudonymization process may serve various purposes, such as regulating access to sensitive information and allowing the sensitive information to be analyzed by third parties. For example, the sensitive data may be personal data, which represents personal information about the public, private and/or professional lives of individuals. In some cases, it may be useful to process pseudonymized data to gather statistical information about the underlying personal information. For example, it may be beneficial to statistically analyze pseudonymized health records (i.e., health records in which sensitive plaintext values have been replaced with corresponding pseudonym values), for purposes of gathering statistical information about certain characteristics (weights, blood pressures, diseases or conditions, diagnoses, and so forth) of particular sectors, or demographics, of the population. The pseudonymization process may, however, potentially alter, if not destroy, statistical properties of the personal information. In other words, a collection of plaintext values may have certain statistical properties that are represented by various statistical measures (means, variances, ranges, distributions, expected and so forth). These statistical properties may not be reflected in the corresponding set of pseudonym values, and accordingly, useful statistical information about the personal information may not be determined from the pseudonymized data.
- As a more specific example, one way to convert plaintext data (e.g., personal data, such as data representing health records, salaries, addresses, and so forth) into a corresponding set of pseudonyms is to encrypt the plaintext data. However, encrypting data may destroy statistical properties of the data. For example, the encryption of plaintext data that has a Gaussian, or normal statistical distribution, may produce a set of pseudonym values, which have an associated uniform probability distribution.
- In accordance with example implementations that are described herein, a pseudonymization process converts plaintext values into corresponding pseudonym values in a process that preserves a statistical distribution of the plaintext values. Moreover, in accordance with example implementations, the pseudonymization process is irreversible. In other words, in accordance with example implementations, it may be quite challenging, if not impossible, to reconstruct the plaintext values from the pseudonym values.
- More specifically, in accordance with example implementations, a pseudonymization engine converts plaintext values (assumed to have a normal statistical distribution) to pseudonym values that have a normal statistical distribution. In accordance with example implementations, the pseudonymization engine repeatedly applies a hash function (a cryptographic hash function, such as an SHA-2 hash function or an SHA-3 hash function, as examples) in the conversion of each plaintext value.
- The output of a hash function is a pseudorandom value. In accordance with the Central Limit Theorem, the sum of several such hash values may approximate or reach a normal, or Gaussian distribution. More specifically, if “H” represents a hash function and “H(x)” represents the application of the hash function to an input value x, the sum H(x)+H(H(x))+H(H(H(x))) approximates, if not exactly matches, a normal distribution. In accordance with example implementations that are described herein, a pseudonym value is determined by repeatedly applying a hash function and adding the resulting hashes together, as set forth above in the summation above. In accordance with example implementations, the resulting set, or collection, of pseudonym values has a predetermined statistical distribution (a Gaussian or normal distribution, as an example); and due to the hash function being a one way function, the pseudonymization may be irreversible.
- Referring to
FIG. 1 , as a more specific example, in accordance with some implementations, acomputer system 100 may include one or multiple hash-based pseudonymization engines 122 (herein called “pseudonymization engines 122”). In general, thecomputer system 100 may be a desktop computer, a server, a client, a tablet computer, a portable computer, a public cloud-based computer system, a private cloud-based computer system, a hybrid cloud-based computer system (i.e., a computer system that has public and private cloud components), a private computer system having multiple computer components disposed on site, a private computer system having multiple computer components geographically distributed over multiple locations, and so forth. - Regardless of its particular form, in accordance with some implementations, the
computer system 100 may include one or multiple processing nodes; and eachprocessing node 110 may include one or multiple personal computers, workstations, servers, rack-mounted computers, special purpose computers, and so forth. Depending on the particular implementations, theprocessing nodes 110 may be located at the same geographical location or may be located at multiple geographical locations. Moreover, in accordance with some implementations,multiple processing nodes 110 may be rack-mounted computers, such that sets of theprocessing nodes 110 may be installed in the same rack. In accordance with further example implementations, theprocessing nodes 110 may be associated with one or multiple virtual machines that are hosted by one or multiple physical machines. - In accordance with some implementations, the
processing nodes 110 may be coupled to astorage 160 of thecomputer system 100 through network fabric (not depicted inFIG. 1 ). In general, the network fabric may include components and use protocols that are associated with any type of communication network, such as (as examples) Fibre Channel networks, iSCSI networks, ATA over Ethernet (AoE) networks, HyperSCSI networks, local area networks (LANs), wide area networks (WANs), global networks (e.g., the Internet), or any combination thereof. - The
storage 160 may include one or multiple physical storage devices that store data using one or multiple storage technologies, such as semiconductor device-based storage, phase change memory-based storage, magnetic material-based storage, memristor-based storage, and so forth. Depending on the particular implementation, the storage devices of thestorage 160 may be located at the same geographical location or may be located at multiple geographical locations. Regardless of its particular form, thestorage 160 may store pseudonymized data records 164 (i.e., data representing pseudonyms, or pseudonym values, generated as described herein). - In accordance with some implementations, a given
processing node 110 may contain apseudonymization engine 122, which is constructed to, for a given plaintext value, repeatedly apply a hash function (a cryptographic hash function, as an example) to produce multiple hash values, which are added together to produce the corresponding pseudonym value, as described herein. Due to the use of a hash function and the corresponding hash values, the pseudonymization process is irreversible, in accordance with example implementations. - In accordance with example implementations, the
processing node 110 may include one or multiplephysical hardware processors 134, such as one or multiple central processing units (CPUs), one or multiple CPU cores, and so forth. Moreover, theprocessing node 110 may include alocal memory 138. In general, thelocal memory 138 is a non-transitory memory that may be formed from, as examples, semiconductor storage devices, phase change storage devices, magnetic storage devices, memristor-based devices, a combination of storage devices associated with multiple storage technologies, and so forth. - Regardless of its particular form, the
memory 138 may store various data 146 (data representing plaintext values, pseudonym values, hash function outputs, mathematical combinations of hash values, intermediate results pertaining to the pseudonymization process, and so forth). Thememory 138 maystore instructions 142 that, when executed by one ormultiple processors 134, cause the processor(s) 134 to form one or multiple components of theprocessing node 110, such as, for example, thepseudonymization engine 122. - In accordance with some implementations, the
pseudonymization engine 122 may be implemented at least in part by a hardware circuit that does not include a processor executing machine executable instructions. In this regard, in accordance with some implementations, thepseudonymization engine 122 may be formed from whole or in part by a hardware processor that does not execute machine executable instructions, such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and so forth. Thus, many implementations are contemplated, which are within the scope of the appended claims. -
FIG. 2 depicts a flow diagram 200 of a process that may be used by thepseudonymization engine 122 for purposes of converting a plaintext value to a pseudonym value, in accordance with example implementations. Referring toFIG. 2 in conjunction withFIG. 1 , pursuant to thetechnique 200, thepseudonymization engine 122 accesses (block 204) data representing a plaintext value. For example, the data may be derived from one of the plaintext data records 164 ofFIG. 1 . Next, thepseudonymization engine 122 determines (block 208) a hash value. In particular, in accordance with example implementations, if the plaintext value is represented by “x,” then block 208 involves thepseudonymization engine 122 applying a hash function (an SHA-2 or SHA-3 hash function, for example), represented by “H,” to the plaintext value x to determine a particular hash value (represented by “H(x)”). Next, pursuant to block 212, in accordance with some implementations, thepseudonymization engine 122 applies the hash function H again. As depicted inblock 212, thepseudonymization engine 122 applies the hash function H to the hash value determined inblock 208 for purposes of determining another hash value, H(H(x)). - As depicted in
FIG. 2 , the above-described process may be repeated, i.e., thepseudonymization engine 122 may apply multiple hash function iterations, where theengine 122, in each iteration, determines a hash based on a result of the previous iteration. In this regard,FIG. 2 depicts, inblock 216, thepseudonymization engine 122 determining another hash value by applying the hash function H to the hash result fromblock 212 to determine a hash value, H(H(H(x))). In accordance with example implementations, thepseudonymization engine 122 may therefore determine three hash values based on the hash function H and the plaintext value x; and then, pursuant to block 220, thepseudonymization engine 122 may determine the pseudonym value, which corresponds to the plaintext value x and is equal to the summation of these three hash values, i.e.,pseudonymization engine 122 may set the pseudonym value equal to H(x)+H(H(x))+H (H(H(x))). - In accordance with further example implementations, the
pseudonymization engine 122 may determine fewer or more than three hash values and base the determination of each pseudonym value on the summation of these hash values. For example, in accordance with further example implementations, thepseudonymization engine 122 may set the pseudonym value equal to H(x)+H(H(x)). - The number of hash function iterations control the statistical distribution of the pseudonym values.
FIG. 3 is a probability function, orstatistical distribution 300, for a set of pseudonym values generated using a single hash function iteration for each pseudonym value. In other words, the pseudonym value for a given plaintext value x is H(x).FIG. 4 is astatistical distribution 400 produced using two hash function iterations. In other words, forFIG. 4 , each plaintext value x is converted to the corresponding pseudonym value by performing two hashes and adding the hashes together, i.e., the pseudonym value is set equal to H(x)+H(H(x)).FIG. 5 is astatistical distribution 500 of pseudonym values produced using three hash function iterations, i.e., a set of pseudonym values produced using thetechnique 200 ofFIG. 2 . As can be seen fromFIGS. 3, 4 and 5 , in accordance with example implementations, with an increasing number of hash function iterations, the corresponding statistical distribution of pseudonym values approaches, if not reaches, a Gaussian, or normal, distribution. - Moreover, in accordance with further example implementations, the
pseudonymization engine 122 may further process a set of pseudonym values that are derived using a summation of hashes (such as one of the summations described above) to further manipulate statistical properties of the pseudonym values. For example, after thepseudonymization engine 122 uses one or multiple hash function iterations to reach or approximate a given distribution, such as a normal distribution, as depicted inFIG. 5 , theengine 122 may then, in accordance with example implementations, scale the data to impart a certain mean and/or variance to the distribution. - The
pseudonymization engine 122 may, in accordance with example implementations, apply a statistical distribution transformation function to the set of intermediate pseudonym values to further manipulate statistical properties of the resulting pseudonym dataset. For example, in accordance with some implementations, thepseudonymization engine 122 may apply a Box Muller or a polar Marsaglia transformation, as just a few examples. In this manner, thepseudonymization engine 122 may, for example, convert a set of intermediate pseudonym values having a normal statistical distribution into a set of pseudonym values that have a log-normal statistical distribution. - Referring to
FIG. 6 , thus, in accordance with example implementations, atechnique 600 includes accessing (block 604) data representing a plurality of plaintext values and converting (block 608) the plaintext values to pseudonym values, which are associated with a predetermined statistical distribution. Converting the plaintext values may include, in accordance with some implementations, for a given plaintext value, repeatedly applying (block 612) a hash function based on the given plaintext value to provide corresponding hash values. Moreover, converting the plaintext values to pseudonyms may include combining (block 616) the hash values to provide a pseudonym value, which corresponds to the given plaintext value. - Referring to
FIG. 7 , in accordance with example implementations, a non-transitory machinereadable storage medium 700 may storeinstructions 718 that, when executed by a machine, cause the machine to access first data representing a plurality of personal data values and process the first data to provide second data, which represents pseudonym values in place of the personal data values. In accordance with example implementations, the processing may include, for a first personal data value, applying a one way conversion function multiple times based on the first personal data value to determine a plurality of intermediate outputs; and combining the intermediate outputs to determine the pseudonym value that corresponds to the first personal data value. - Referring to
FIG. 8 , in accordance with example implementations, anapparatus 800 includes at least oneprocessor 820 and amemory 810 to storeinstructions 814 that, when executed by the processor(s) 820, cause the processor(s) 820 to convert a first dataset representing plaintext data and having a first statistical property to a second dataset, which represents pseudonyms that has the first statistical property. The conversion includes, for a first plaintext value that is represented by the plaintext data, generating a plurality of hashes based on the first plaintext value; and determining a pseudonym, which corresponds to the first plaintext value based on the plurality of hashes. - While the present disclosure has been described with respect to a limited number of embodiments, those skilled in the art, having the benefit of this disclosure, will appreciate numerous modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations.
Claims (20)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/926,392 US20190294820A1 (en) | 2018-03-20 | 2018-03-20 | Converting plaintext values to pseudonyms using a hash function |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/926,392 US20190294820A1 (en) | 2018-03-20 | 2018-03-20 | Converting plaintext values to pseudonyms using a hash function |
Publications (1)
Publication Number | Publication Date |
---|---|
US20190294820A1 true US20190294820A1 (en) | 2019-09-26 |
Family
ID=67985370
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/926,392 Abandoned US20190294820A1 (en) | 2018-03-20 | 2018-03-20 | Converting plaintext values to pseudonyms using a hash function |
Country Status (1)
Country | Link |
---|---|
US (1) | US20190294820A1 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11516658B2 (en) * | 2018-07-03 | 2022-11-29 | Board Of Regents, The University Of Texas System | Efficient and secure distributed signing protocol for mobile devices in wireless networks |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020035622A1 (en) * | 2000-06-07 | 2002-03-21 | Barber Timothy P. | Online machine data collection and archiving process |
US20050235154A1 (en) * | 1999-06-08 | 2005-10-20 | Intertrust Technologies Corp. | Systems and methods for authenticating and protecting the integrity of data streams and other data |
US20100131969A1 (en) * | 2008-04-28 | 2010-05-27 | Justin Tidwell | Methods and apparatus for audience research in a content-based network |
US20110307691A1 (en) * | 2008-06-03 | 2011-12-15 | Institut Telecom-Telecom Paris Tech | Method of tracing and of resurgence of pseudonymized streams on communication networks, and method of sending informative streams able to secure the data traffic and its addressees |
US20130177155A1 (en) * | 2012-10-05 | 2013-07-11 | Comtech Ef Data Corp. | Method and System for Generating Normal Distributed Random Variables Based On Cryptographic Function |
US20140165215A1 (en) * | 2012-12-12 | 2014-06-12 | Vmware, Inc. | Limiting access to a digital item |
US20160342737A1 (en) * | 2015-05-22 | 2016-11-24 | The University Of British Columbia | Methods for the graphical representation of genomic sequence data |
US20170134459A1 (en) * | 2015-11-09 | 2017-05-11 | T-Mobile Usa, Inc. | Preference-aware content streaming |
US20180082082A1 (en) * | 2016-09-21 | 2018-03-22 | Mastercard International Incorporated | Method and system for double anonymization of data |
-
2018
- 2018-03-20 US US15/926,392 patent/US20190294820A1/en not_active Abandoned
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050235154A1 (en) * | 1999-06-08 | 2005-10-20 | Intertrust Technologies Corp. | Systems and methods for authenticating and protecting the integrity of data streams and other data |
US20020035622A1 (en) * | 2000-06-07 | 2002-03-21 | Barber Timothy P. | Online machine data collection and archiving process |
US20100131969A1 (en) * | 2008-04-28 | 2010-05-27 | Justin Tidwell | Methods and apparatus for audience research in a content-based network |
US20110307691A1 (en) * | 2008-06-03 | 2011-12-15 | Institut Telecom-Telecom Paris Tech | Method of tracing and of resurgence of pseudonymized streams on communication networks, and method of sending informative streams able to secure the data traffic and its addressees |
US20130177155A1 (en) * | 2012-10-05 | 2013-07-11 | Comtech Ef Data Corp. | Method and System for Generating Normal Distributed Random Variables Based On Cryptographic Function |
US20140165215A1 (en) * | 2012-12-12 | 2014-06-12 | Vmware, Inc. | Limiting access to a digital item |
US20160342737A1 (en) * | 2015-05-22 | 2016-11-24 | The University Of British Columbia | Methods for the graphical representation of genomic sequence data |
US20170134459A1 (en) * | 2015-11-09 | 2017-05-11 | T-Mobile Usa, Inc. | Preference-aware content streaming |
US20180082082A1 (en) * | 2016-09-21 | 2018-03-22 | Mastercard International Incorporated | Method and system for double anonymization of data |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11516658B2 (en) * | 2018-07-03 | 2022-11-29 | Board Of Regents, The University Of Texas System | Efficient and secure distributed signing protocol for mobile devices in wireless networks |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20200403778A1 (en) | Dynamic blockchain system and method for providing efficient and secure distributed data access, data storage and data transport | |
US20200265155A1 (en) | Data protection via aggregation-based obfuscation | |
US10454901B2 (en) | Systems and methods for enabling data de-identification and anonymous data linkage | |
US11777729B2 (en) | Secure analytics using term generation and homomorphic encryption | |
CN107683481B (en) | Computing encrypted data using delayed evaluation | |
US11681719B2 (en) | Efficient access of chainable records | |
EP3794487A1 (en) | Obfuscation and deletion of personal data in a loosely-coupled distributed system | |
US20230205925A1 (en) | Generating hash values for input strings | |
US11106821B2 (en) | Determining pseudonym values using tweak-based encryption | |
Gupta et al. | Faster as well as early measurements from big data predictive analytics model | |
US20200233977A1 (en) | Classification and management of personally identifiable data | |
Lin et al. | Privacy-preserving similarity search with efficient updates in distributed key-value stores | |
US11138338B2 (en) | Statistical property preserving pseudonymization | |
US11115216B2 (en) | Perturbation-based order preserving pseudonymization of data | |
US20190294820A1 (en) | Converting plaintext values to pseudonyms using a hash function | |
WO2022071997A1 (en) | Reconstructing time series datasets with missing values utilizing machine learning | |
Lam et al. | Gpu-based private information retrieval for on-device machine learning inference | |
WO2019211437A1 (en) | Computational efficiency in symbolic sequence analytics using random sequence embeddings | |
US11647004B2 (en) | Learning to transform sensitive data with variable distribution preservation | |
US20210350015A1 (en) | Secure data replication in distributed data storage environments | |
CN112528327A (en) | Data desensitization method and device and data restoration method and device | |
US10956610B2 (en) | Cycle walking-based tokenization | |
US20200074110A1 (en) | Sampling from a remote dataset with a private criterion | |
US20240004610A1 (en) | String similarity based weighted min-hashing | |
US11632380B2 (en) | Identifying large database transactions |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: ENTIT SOFTWARE LLC, CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MARTIN, LUTHER;ROAKE, TIMOTHY;REEL/FRAME:045289/0587 Effective date: 20180312 |
|
AS | Assignment |
Owner name: MICRO FOCUS LLC, CALIFORNIA Free format text: CHANGE OF NAME;ASSIGNOR:ENTIT SOFTWARE LLC;REEL/FRAME:050004/0001 Effective date: 20190523 |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NON FINAL ACTION MAILED |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER |
|
AS | Assignment |
Owner name: JPMORGAN CHASE BANK, N.A., NEW YORK Free format text: SECURITY AGREEMENT;ASSIGNORS:MICRO FOCUS LLC;BORLAND SOFTWARE CORPORATION;MICRO FOCUS SOFTWARE INC.;AND OTHERS;REEL/FRAME:052295/0041 Effective date: 20200401 Owner name: JPMORGAN CHASE BANK, N.A., NEW YORK Free format text: SECURITY AGREEMENT;ASSIGNORS:MICRO FOCUS LLC;BORLAND SOFTWARE CORPORATION;MICRO FOCUS SOFTWARE INC.;AND OTHERS;REEL/FRAME:052294/0522 Effective date: 20200401 |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: FINAL REJECTION MAILED |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: FINAL REJECTION MAILED |
|
STCV | Information on status: appeal procedure |
Free format text: NOTICE OF APPEAL FILED |
|
STCV | Information on status: appeal procedure |
Free format text: APPEAL BRIEF (OR SUPPLEMENTAL BRIEF) ENTERED AND FORWARDED TO EXAMINER |
|
STCV | Information on status: appeal procedure |
Free format text: EXAMINER'S ANSWER TO APPEAL BRIEF MAILED |
|
STCV | Information on status: appeal procedure |
Free format text: ON APPEAL -- AWAITING DECISION BY THE BOARD OF APPEALS |
|
AS | Assignment |
Owner name: NETIQ CORPORATION, WASHINGTON Free format text: RELEASE OF SECURITY INTEREST REEL/FRAME 052295/0041;ASSIGNOR:JPMORGAN CHASE BANK, N.A.;REEL/FRAME:062625/0754 Effective date: 20230131 Owner name: MICRO FOCUS SOFTWARE INC. (F/K/A NOVELL, INC.), MARYLAND Free format text: RELEASE OF SECURITY INTEREST REEL/FRAME 052295/0041;ASSIGNOR:JPMORGAN CHASE BANK, N.A.;REEL/FRAME:062625/0754 Effective date: 20230131 Owner name: MICRO FOCUS LLC, CALIFORNIA Free format text: RELEASE OF SECURITY INTEREST REEL/FRAME 052295/0041;ASSIGNOR:JPMORGAN CHASE BANK, N.A.;REEL/FRAME:062625/0754 Effective date: 20230131 Owner name: NETIQ CORPORATION, WASHINGTON Free format text: RELEASE OF SECURITY INTEREST REEL/FRAME 052294/0522;ASSIGNOR:JPMORGAN CHASE BANK, N.A.;REEL/FRAME:062624/0449 Effective date: 20230131 Owner name: MICRO FOCUS SOFTWARE INC. (F/K/A NOVELL, INC.), WASHINGTON Free format text: RELEASE OF SECURITY INTEREST REEL/FRAME 052294/0522;ASSIGNOR:JPMORGAN CHASE BANK, N.A.;REEL/FRAME:062624/0449 Effective date: 20230131 Owner name: MICRO FOCUS LLC, CALIFORNIA Free format text: RELEASE OF SECURITY INTEREST REEL/FRAME 052294/0522;ASSIGNOR:JPMORGAN CHASE BANK, N.A.;REEL/FRAME:062624/0449 Effective date: 20230131 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- AFTER EXAMINER'S ANSWER OR BOARD OF APPEALS DECISION |